A complete understanding of human behavior and disease depends upon our ability to parse genetic and environmental influences in the human brain. The heritability of a trait quantifies the degree of its variability due to genetic influences. Classical approach for quantifying heritability operate on simple traits, and sometimes do not properly model and control for other potential sources of variation, such as age or sex. We therefore develop Causal Heritability of Networks (CHaiN) to rigorously quantify heritability of human brain networks (i.e., connectomes). We applied CHaiN to 1024 anatomical connectomes derived from the Human Connectome Project. Connectomes appeared to be heritable, but heritability was insignificant once we addressed variability within networks. These results suggest that previous conclusions on connectome heritability may be driven by the shared network structures, and highlights the importance of modeling networks and other sources of variability when studying heritability of connectomes.

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

We develop an open-source tool for the retrospective estimation of inter-volume head-motion and eddy-current distortions, typically found in diffusion MRI (dMRI) data acquired with echo-planar imaging schemes. The implementation is “open-since-inception” to ensure transparency. By leveraging the widely used DIPY package and a user-friendly interface, researchers have at their disposal an implementation combining state-of-art approaches with substantial improvements that can efficiently leverage any compliant diffusion model(s) while simultaneously accounting for susceptibility distortions.

Using brain atlases to localize regions of interest is a requirement for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization. Standardization can facilitate consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Our group has worked to consolidate an extensive selection of popular human brain atlases into a single, curated, open-source library, where they are stored following a standardized protocol with accompanying metadata, which can serve as the basis for future atlases. The repository containing the atlases, the specification, as well as relevant transformation functions is available in the neuroparc OSF registered repository or https://github.com/neurodata/neuroparc .

Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the brain. Research in human connectomics, leveraging functional and diffusion Magnetic Resonance Imaging (MRI), is a resource-intensive practice. Typical analysis routines require significant computational capabilities and subject matter expertise. Establishing a pipeline that is low-resource, easy to use, and off-the-shelf (can be applied across multifarious datasets without parameter tuning to reliably estimate plausible connectomes), would significantly lower the barrier to entry into connectomics, thereby democratizing the field by empowering a more diverse and inclusive community of connectomists. We therefore introduce ‘MRI to Graphs’ ( m2g ). To illustrate its properties, we used m2g to process MRI data from 35 different studies (≈ 6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that adhered to established properties, such as stronger ipsilateral than contralateral connections in structural connectomes, and stronger homotopic than heterotopic correlations in functional connectomes. Moreover, the connectomes estimated by m2g are more similar within individuals than between them, suggesting that m2g preserves biological variability. m2g is portable, and can run on a single CPU with 16 GB of RAM in less than a couple hours, or be deployed on the cloud using its docker container. All code is available on https://github.com/neurodata/m2g and documentation is available on docs.neurodata.io/m2g.

In this chapter, we explore Support Vector Machine (SVM)—a machine learning method that has become exceedingly popular for neuroimaging analysis in recent years. Because of their relative simplicity and flexibility for addressing a range of classification problems, SVMs distinctively afford balanced predictive performance, even in studies where sample sizes may be limited. In brain disorders research, SVMs are typically employed using multivoxel pattern analysis (MVPA) because their relative simplicity carries a lower risk of overfitting even using high-dimensional imaging data. More recently, SVMs have been used in the context of precision psychiatry, particularly for applications that involve predicting diagnosis and prognosis of brain diseases such as Alzheimer's disease, schizophrenia, and depression. In the last section of this chapter, we review a number of recent studies that use SVM for such applications.

Background: Mutations in the progranulin ( GRN) gene are a major cause of familial frontotemporal dementia. They result in a loss of progranulin levels and in GRN-related brain degenerative changes that unfold over years if not decades. The aim of our review was to summarize the evidence on emerging functional and structural brain abnormalities in carriers of GRN mutations. Summary: We performed a systematic search for studies that used at least one modality ( structural MRI, fMRI, fluorodeoxyglucose positron emission tomography, diffusion tensor imaging) to compare mutation carriers to non-carrier controls. Our search produced 13 studies published between 2008 and 2017, the majority cross-sectional, with carrier sample sizes ranging from 5 to 65. Key Messages: The aggregate findings suggest that ( 1) measurable brain changes are detectable in at least some mutation carriers 20-25 years prior to disease onset; ( 2) functional/metabolic changes progress more consistently over time than structural changes; ( 3) the topographic pattern is anterior to posterior, not always asymmetric, and maps onto known functional networks. (c) 2019 S. Karger AG, Basel

Brooding, which refers to a repetitive focus on one's distress, is associated with functional connectivity within Default-Mode, Salience, and Executive-Control networks (DMN; SN; ECN), comprising the so-called "triple-network" of attention. Individual differences in brain structure that might perseverate dysfunctional connectivity of brain networks associated with brooding are less clear, however. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between brooding severity, white-matter microstructure, and resting-state functional connectivity in depressed adults (N = 32-44), and then examined whether findings directly replicated in a demographically-similar, independent sample (N = 36-45). Among the fully-replicated results, three core findings emerged. First, brooding severity is associated with functional integration and segregation of the triple-network, particularly with a Precuneal subnetwork of the DMN. Second, microstructural asymmetry of the Superior Longitudinal Fasciculus (SLF) provides a robust structural connectivity basis for brooding and may account for over 20% of its severity (Discovery: adj. R2 = 0.18; Replication: adj. R2 = 0.22; MSE = 0.06, Predictive R2 = 0.22). Finally, microstructure of the right SLF and auxiliary white-matter is associated with the functional connectivity correlates of brooding, both within and between components of the triple-network (Discovery: adj. R2 = 0.21; Replication: adj. R2 = 0.18; MSE = 0.03, Predictive R2 = 0.21-0.22). By cross-validating multimodal discovery with replication, the present findings help to reproducibly unify disparate perspectives of brooding etiology. Based on that synthesis, our study reformulates brooding as a microstructural-functional connectivity neurophenotype.

Background: Mutations in the progranulin (GRN) gene are a major cause of familial frontotemporal dementia. They result in a loss of progranulin levels and in GRN-related brain degenerative changes that unfold over years if not decades. The aim of our review was to summarize the evidence on emerging functional and structural brain abnormalities in carriers of GRN mutations. Summary: We performed a systematic search for studies that used at least one modality (structural MRI, fMRI, fluorodeoxyglucose positron emission tomography, diffusion tensor imaging) to compare mutation carriers to non-carrier controls. Our search produced 13 studies published between 2008 and 2017, the majority cross-sectional, with carrier sample sizes ranging from 5 to 65. Key Messages: The aggregate findings suggest that (1) measurable brain changes are detectable in at least some mutation carriers 20–25 years prior to disease onset; (2) functional/metabolic changes progress more consistently over time than structural changes; (3) the topographic pattern is anterior to posterior, not always asymmetric, and maps onto known functional networks.

Theoretical models and empirical research point to negatively biased attention as a maintaining factor in depression. Although preliminary studies suggest experimentally modifying attentional biases (i.e., attentional bias modification; ABM) reduces depression symptoms and depression risk, relatively few rigorous studies with clinical samples have been completed. This clinical trial examines the impact of ABM on a sample of adults (N = 123) with elevated depression severity who also exhibit at least modest levels of negatively biased attention prior to treatment. Participants will be randomly assigned to either active ABM, placebo ABM, or an assessment-only control condition. Individuals assigned to ABM will complete 5 trainings per week (2 in-clinic, 3 brief trainings at-home) during a four-week period. Throughout this four-week period, participants will complete weekly assessments of symptom severity and putative treatment mediators measured across different levels of analysis (e.g., eye tracking, behavioral measures, and functional Magnetic Resonance Imaging). This article details the rationale and design of the clinical trial, including methodological issues that required more extensive consideration. Our findings may not only point to an easily-accessible, efficacious treatment for depression but may also provide a meaningful test of whether a theoretically important construct, negatively biased attention, maintains depression.

Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of PTSD risk after trauma exposure is a critical precursor to large-scale deployment of early interventions that may reduce individual suffering and societal costs. Toward this aim, we applied ensemble machine learning to predict PTSD status three months after severe injury using cost-effective and minimally invasive data. Participants (N = 271) were recruited at a Level 1 Trauma Center where they provided variables routinely collected at the hospital, including pulse, injury severity, and demographics, as well as psychological variables, including self-reported current depression, psychiatric history, and social support. Participant zip codes were used to extract contextual variables including population total and density, average annual income, and health insurance coverage rates from publicly available U.S. Census data. Machine learning yielded good prediction of PTSD status 3 months post-hospitalization, AUC = 0.85 95% CI [0.83, 0.86], and significantly outperformed all benchmark comparison models in a cross-validation procedure designed to yield an unbiased estimate of performance. These results demonstrate that good prediction can be attained from variables that individually have relatively weak predictive value, pointing to the promise of ensemble machine learning approaches that do not rely on strong isolated risk factors.

Depressive Rumination (DR), which involves a repetitive focus on one’s distress, has been linked to alterations in functional connectivity of the ‘triple-network’, consisting of Default-Mode, Salience, and Executive Control networks. A structural basis for these functional alterations that can dually explain DR’s persistence as a stable trait remains unexplored, however. Using diffusion and functional Magnetic Resonance Imaging, we investigated multimodal relationships between DR severity, white-matter microstructure, and resting-state functional connectivity in depressed adults, and then directly replicated our results in a phenotypically-matched, independent sample (total N = 78). Among the fully-replicated findings, DR severity was associated with: (a) global microstructure of the right Superior Longitudinal Fasciculus and local microstructure of distributed primary-fiber and crossing-fiber white-matter; (b) an imbalance of functional connectivity segregation and integration of the triple-network; and (c) ‘multi-layer’ associations linking these microstructural and functional connectivity biomarkers to one another. Taken together, the results provide reproducible evidence for a multi-layer, microstructural-functional network model of rumination in the depressed brain.

AbstractBackgroundSome Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.MethodAn elastic net and random forest were trained to predict depression symptoms and related disability after an 8-week course of an Internet intervention, Deprexis, involving adults (N = 283) from across the USA. Candidate predictors included psychopathology, demographics, treatment expectancies, treatment usage, and environmental context obtained from population databases. Model performance was evaluated using predictive R2 $\lpar R_{{\rm pred}}^2\rpar\comma $ the expected variance explained in a new sample, estimated by 10 repetitions of 10-fold cross-validation.ResultsAn ensemble model was created by averaging the predictions of the elastic net and random forest. Model performance was compared with a benchmark linear autoregressive model that predicted each outcome using only its baseline. The ensemble predicted more variance in post-treatment depression (8.0% gain, 95% CI 0.8–15; total $R_{{\rm pred}}^2 \; $= 0.25), disability (5.0% gain, 95% CI −0.3 to 10; total $R_{{\rm pred}}^2 \; $= 0.25), and well-being (11.6% gain, 95% CI 4.9–19; total $R_{{\rm pred}}^2 \; $= 0.29) than the benchmark model. Important predictors included comorbid psychopathology, particularly total psychopathology and dysthymia, low symptom-related disability, treatment credibility, lower access to therapists, and time spent using certain Deprexis modules.ConclusionA number of variables predict symptom improvement following an Internet intervention, but each of these variables makes relatively small contributions. Machine learning ensembles may be a promising statistical approach for identifying the cumulative contribution of many weak predictors to psychosocial depression treatment response.

Individuals differ in their ability to understand emotional information and apply that understanding to make decisions and solve problems effectively - a construct known as Emotional Intelligence (EI). While considerable evidence supports the importance of EI in social and occupational functioning, the neural underpinnings of this capacity are relatively unexplored. We used Tract-Based Spatial Statistics (TBSS) to determine the white matter correlates of EI as measured by the ability-based Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Participants included 32 healthy adults (16 men; 16 women), aged 18-45 years. White matter integrity in key tracts was positively correlated with the Strategic Area branches of the MSCEIT (Understanding Emotions and Managing Emotions), but not the Experiential branches (Perceiving and Facilitating Emotions). Specifically, the Understanding Emotions branch was associated with greater fractional anisotropy (FA) within somatosensory and sensory-motor fiber bundles, particularly those of the left superior longitudinal fasciculus and corticospinal tract. Managing Emotions was associated with greater FA within frontal-affective association tracts including the anterior forceps and right uncinate fasciculus, along with frontal-parietal cingulum and interhemispheric corpus callosum tracts. These findings suggest that specific components of EI are directly related to the structural microarchitecture of major axonal pathways.

STUDY OBJECTIVES Prolonged exposure to blue wavelength light has been shown to have an alerting effect, and enhances performance on cognitive tasks. A small number of studies have also shown that relatively short exposure to blue light leads to changes in functional brain responses during the period of exposure. The extent to which blue light continues to affect brain functioning during a cognitively challenging task after cessation of longer periods of exposure (i.e., roughly 30 minutes or longer), however, has not been fully investigated. METHODS A total of 35 healthy participants (18 female) were exposed to either blue (469 nm) (n = 17) or amber (578 nm) (n = 18) wavelength light for 30 minutes in a darkened room, followed immediately by functional magnetic resonance imaging (fMRI) while undergoing a working memory task (N-back task). RESULTS Participants in the blue light condition were faster in their responses on the N-back task and showed increased activation in the dorsolateral (DLPFC) and ventrolateral (VLPFC) prefrontal cortex compared to those in the amber control light condition. Furthermore, greater activation within the VLPFC was correlated with faster N-back response times. CONCLUSIONS This is the first study to suggest that a relatively brief, single exposure to blue light has a subsequent beneficial effect on working memory performance, even after cessation of exposure, and leads to temporarily persisting functional brain changes within prefrontal brain regions associated with executive functions. These findings may have broader implication for using blue-enriched light in a variety of work settings where alertness and quick decision-making are important.